https://github.com/tensorflow/lingvo/blob/014007fbed690f6a10b51b630d7a4d0dd6cfd5df/lingvo/tasks/car/params/waymo_deepfusion.py
Learning an Uncertainty-Aware Object Detector for Autonomous Driving
Papers with Code 아카이브
Autonomous Driving
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- 평가표
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- 보존된 평가 기록
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Autonomous driving is the task of driving a vehicle without human conduction.
Many of the state-of-the-art results can be found at more general task pages such as 3D Object Detection and Semantic Segmentation.
<span style="color:grey; opacity: 0.5">(Image credit: Exploring the Limitations of Behavior Cloning for Autonomous Driving)</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/testingautomated-usi/surprise-adequacy
Guiding Deep Learning System Testing using Surprise Adequacy
https://github.com/texasinstruments/edgeai-yolox
https://github.com/thoughtworks-hpc/PINetTensorrt
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
https://github.com/thouyang/ccd-dsa
Guiding Deep Learning System Testing using Surprise Adequacy
https://github.com/tiagocortinhal/titan-next
https://github.com/tianweiy/CenterPoint-KITTI
https://github.com/traveller59/second.pytorch
https://github.com/tsinghua-mars-lab/futr3d
https://github.com/tsinghua-mars-lab/online-hd-map-construction-cvpr2023
HDMapNet: An Online HD Map Construction and Evaluation Framework
https://github.com/tu-rbo/concarne
https://github.com/tue-mps/vfm-uda
https://github.com/tum-traffic-dataset/coopdet3d
https://github.com/tuncalie/sim-atav
https://github.com/tuscan-chicken-wrap/ECE260FinalProject
https://github.com/uditsharma29/flynet-cann
A Hybrid Compact Neural Architecture for Visual Place Recognition
https://github.com/umautobots/bidireaction-trajectory-prediction
BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal Estimation
https://github.com/umautobots/failing-to-learn
Failing to Learn: Autonomously Identifying Perception Failures for Self-driving Cars
https://github.com/umich-curly/3dmapping
https://github.com/unveilx/slm-od-ml-comparison
Comparison Of Deep Object Detectors On A New Vulnerable Pedestrian Dataset
https://github.com/ut-amrl/pips
Robot Action Selection Learning via Layered Dimension Informed Program Synthesis
https://github.com/ut-austin-rpl/coopernaut
COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles
https://github.com/vahidkhosh/squeezeDet
https://github.com/vahidkhosh/squeezedet-keras
https://github.com/valeoai/NeeDrop
NeeDrop: Self-supervised Shape Representation from Sparse Point Clouds using Needle Dropping
https://github.com/valeoai/pointbev
https://github.com/vectorch-ai/scalellm
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/venkateshdudipalli/Self-Driving-Car
Virtual to Real Reinforcement Learning for Autonomous Driving
https://github.com/vijaylaxmid/DeepLearning_3DPointClouds
https://github.com/vision-agh/pointpillars-backbone
Exploring Data Augmentation for Multi-Modality 3D Object Detection
https://github.com/vision-agh/pointpillars_backbone
Exploring Data Augmentation for Multi-Modality 3D Object Detection
https://github.com/vita-epfl/s-attack
Are socially-aware trajectory prediction models really socially-aware?
https://github.com/vita-epfl/semdisc
Are socially-aware trajectory prediction models really socially-aware?
https://github.com/vita-epfl/social-nce-crowdnav
Social NCE: Contrastive Learning of Socially-aware Motion Representations
https://github.com/vita-epfl/unitraj
Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction
https://github.com/vlislab22/360_Survey
Deep Learning for Omnidirectional Vision: A Survey and New Perspectives
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.